| ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS |
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Stabilized adaptive waveform inversion for enhanced robustness in Gaussian penalty matrix parameterization and transcranial ultrasound imaging |
| Jun-Jie Zhao(赵俊杰)1,†, Shan-Mu Jin(金山木)2,†, Yue-Kun Wang(王月坤)2, Yu Wang(王裕)2,‡, and Ya-Hui Peng(彭亚辉)1,§ |
1 School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;
2 Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China |
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Abstract Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases, preoperative planning of craniotomies and intraoperative management during craniotomy procedures. Adaptive waveform inversion (AWI), a variant of full waveform inversion (FWI), has shown potential in intracranial ultrasound imaging. However, the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios. Conventional AWI struggles to produce accurate images in these cases, limiting its application in critical medical settings. To address these issues, we propose a stabilized adaptive waveform inversion (SAWI) method, which introduces a user-defined zero-lag position for the Wiener filter. Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails, perform successful transcranial imaging in configurations where AWI cannot, and maintain the same imaging accuracy as AWI. The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs, which helps to promote the application of AWI in medical fields, particularly in transcranial scenarios.
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Received: 23 January 2025
Revised: 29 April 2025
Accepted manuscript online: 07 May 2025
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PACS:
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43.60.Lq
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(Acoustic imaging, displays, pattern recognition, feature extraction)
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43.80.Qf
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(Medical diagnosis with acoustics)
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43.35.Wa
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(Biological effects of ultrasound, ultrasonic tomography)
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87.63.dh
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(Ultrasonographic imaging)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant No. 82151302), the National High Level Hospital Clinical Research Funding (Grant No. 2022-PUMCH-B-113), the National High Level Hospital Clinical Research Funding (Grant No. 2022-PUMCH-A-019), and the CAMS Innovation Fund for Medical Sciences (Grant No. 2021-12M-1-014). |
Corresponding Authors:
Yu Wang, Ya-Hui Peng
E-mail: ywang@pumch.cn;yhpeng@bjtu.edu.cn
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Cite this article:
Jun-Jie Zhao(赵俊杰), Shan-Mu Jin(金山木), Yue-Kun Wang(王月坤), Yu Wang(王裕), and Ya-Hui Peng(彭亚辉) Stabilized adaptive waveform inversion for enhanced robustness in Gaussian penalty matrix parameterization and transcranial ultrasound imaging 2025 Chin. Phys. B 34 084301
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[1] Ivanov M, Wilkins S, Poeata I and Brodbelt A 2010 Br. J. Neurosurg. 24 510
[2] Smith S W, Ivancevich N M, Lindsey B D, Whitman J, Light E, Fronheiser M, Nicoletto H A and Laskowitz D T 2009 Ultrasound Med. Biol. 35 329
[3] Guasch L, Calderón Agudo O, Tang M X, Nachev P and Warner M 2020 NPJ Digit. Med. 3 28
[4] Edlow B L, Mareyam A, Horn A, Polimeni J R, Witzel T, Tisdall M D, Augustinack J C, Stockmann J P, Diamond B R, Stevens A, Tirrell L S, Folkerth R D,Wald L L, Fischl B and van der Kouwe A 2019 Sci. Data 6 244
[5] Yanagawa M, Hata A, Honda O, Kikuchi N, Miyata T, Uranishi A, Tsukagoshi S and Tomiyama N 2018 Eur. Radiol. 28 5060
[6] Menikou G, Dadakova T, Pavlina M, Bock M and Damianou C 2015 Ultrasonics 57 144
[7] Chen T, Liu L, Ma X, Zhang Y, Liu H, Zheng R, Ren J, Zhou H, Ren Y, Gao R, Chen N, Zheng H, Song L and Liu C 2022 IEEE Trans. Biomed. Eng. 69 1093
[8] Emberson J, Lees K R, Lyden P, Blackwell L, Albers G, Bluhmki E, Brott T, Cohen G, Davis S, Donnan G, Grotta J, Howard G, Kaste M, Koga M, von Kummer R, Lansberg M, Lindley R I, Murray G, Olivot J M, Parsons M, Tilley B, Toni D, Toyoda K, Wahlgren N, Wardlaw J, Whiteley W, del Zoppo G J and Baigent C 2014 Lancet 384 1929
[9] Lauzier P T and Chen G H 2013 Med. Phys. 40 021902
[10] Comeau R M, Sadikot A F, Fenster A and Peters T M 2000 Med. Phys. 27 787
[11] Letteboer M M, Willems P W, Viergever M A and Niessen W J 2005 IEEE Trans. Biomed. Eng. 52 268
[12] Maurer C R Jr, Hill D L, Martin A J, Liu H, McCue M, Rueckert D, Lloret D, Hall W A, Maxwell R E, Hawkes D J and Truwit C L 1998 IEEE Trans. Med. Imaging 17 817
[13] Nimsky C, Ganslandt O, Cerny S, Hastreiter P, Greiner G and Fahlbusch R 2000 Neurosurgery 47 1070
[14] Guasch L, Warner M and Ravaut C 2019 Geophysics 84 R447
[15] Ren J, Li J, Chen S, Liu Y and Ta D 2025 Ultrasonics 145 107465
[16] Wiskin JW, Borup D T, Iuanow E, Klock J and Lenox M W 2017 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 64 1161
[17] Sandhu G Y, Li C, Roy O, Schmidt S and Duric N 2015 Phys. Med. Biol. 60 5381
[18] Wiskin J, Malik B, Borup D, Pirshafiey N and Klock J 2020 Sci. Rep. 10 20166
[19] Li Y B, Wang J, Su C, Lin W J, Wang X M and Luo Y 2023 Chin. Phys. B 32 014303
[20] Bunks C, Saleck F M, Zaleski S and Chavent G 1995 Geophysics 60 1457
[21] Jiang C, Li B, Xie L, Liu C, Xu K, Zhan Y and Ta D 2023 Ultrasonics 135 107124
[22] Ren J, Wang X, Liu C, Sun H, Tong J, Lin M, Li J, Liang L, Yin F, Xie M and Liu Y 2023 Sensors 23 8341
[23] Robins T, Camacho J, Agudo O C, Herraiz J L and Guasch L 2021 Sensors 21 4570
[24] Yao J 2019 Adaptive Waveform Inversion: Algebraic Interpretation, Convergence Analysis and Alternative Formulations (PhD Thesis) (London: Imperial College London)
[25] Pladys A, Brossier R, Li Y and Métivier L 2021 Geophysics 86 R563
[26] Chen W, Tao C, Hu Z, Yuan S, Liu Q and Liu X 2022 Chin. Phys. B 31 044304
[27] Warner M and Guasch L 2016 Geophysics 81 R429
[28] Courant R, Friedrichs K and Lewy H 1967 IBM J. Res. Dev. 11 215
[29] Iacono M I, Neufeld E, Akinnagbe E, Bower K, Wolf J, Vogiatzis Oikonomidis I, Sharma D, Lloyd B,Wilm B J,Wyss M, Pruessmann K P, Jakab A, Makris N, Cohen E D, Kuster N, Kainz W and Angelone L M 2015 PLoS One 10 e0124126
[30] Cueto C, Bates O, Strong G, Cudeiro J, Luporini F, Calderoń Agudo O, Gorman G, Guasch L and Tang M X 2022 Comput. Methods Programs Biomed. 240 107710
[31] Eaid M, Keating S and Innanen K https://www.crewes.org/Documents/ResearchReports/2018/CRR201809.pdf
[32] Li Y, Shi Q, Li Y, Song X, Liu C, Ta D and Wang W 2021 Chin. Phys. B 30 014302 |
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